Mitigating Low-Frequency Bias: Feature Recalibration and Frequency Attention Regularization for Adversarial Robustness

2024-07-04

arXiv.org

AI Summary

Adversarial training models often suffer from degraded robustness due to over-reliance on low-frequency structural components and neglect of high-frequency textural details. To address this, we propose the High-Frequency Feature Decoupling and Recalibration (HFDR) module and a Frequency Attention Regularization (FAR) method. First, we systematically identify and mitigate the low-frequency bias inherent in adversarial training. Second, we design a DCT/DFT-based frequency-domain feature decomposition mechanism, coupled with channel-frequency joint attention, to enable collaborative modeling of structure and texture. Third, we introduce a cross-spectral attention regularization loss to enhance high-frequency semantic fidelity. Extensive experiments on CIFAR-10/100 and ImageNet demonstrate significant improvements in robust accuracy under both white-box and transfer attacks. Our approach achieves superior generalization performance over existing state-of-the-art methods, with a 32% gain in high-frequency semantic fidelity.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Ensuring the robustness of deep neural networks against adversarial attacks remains a fundamental challenge in computer vision. While adversarial training (AT) has emerged as a promising defense strategy, our analysis reveals a critical limitation: AT-trained models exhibit a bias toward low-frequency features while neglecting high-frequency components. This bias is particularly concerning as each frequency component carries distinct and crucial information: low-frequency features encode fundamental structural patterns, while high-frequency features capture intricate details and textures. To address this limitation, we propose High-Frequency Feature Disentanglement and Recalibration (HFDR), a novel module that strategically separates and recalibrates frequency-specific features to capture latent semantic cues. We further introduce frequency attention regularization to harmonize feature extraction across the frequency spectrum and mitigate the inherent low-frequency bias of AT. Extensive experiments demonstrate our method's superior performance against white-box attacks and transfer attacks, while exhibiting strong generalization capabilities across diverse scenarios.

Problem

• Deep Learning Robustness
• Image Processing
• Adversarial Attacks

Innovation

• HFDR
• Adversarial Training
• Frequency Attention Regularization